Articles | Volume 21, issue 10
https://doi.org/10.5194/nhess-21-3199-2021
https://doi.org/10.5194/nhess-21-3199-2021
Research article
 | 
27 Oct 2021
Research article |  | 27 Oct 2021

Improving flood damage assessments in data-scarce areas by retrieval of building characteristics through UAV image segmentation and machine learning – a case study of the 2019 floods in southern Malawi

Lucas Wouters, Anaïs Couasnon, Marleen C. de Ruiter, Marc J. C. van den Homberg, Aklilu Teklesadik, and Hans de Moel

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2020-417', Anonymous Referee #1, 31 Mar 2021
  • RC2: 'Review report for manuscript nhess-2020-417', Anonymous Referee #2, 07 Apr 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (07 Jun 2021) by Sven Fuchs
AR by Lucas Wouters on behalf of the Authors (04 Aug 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (09 Aug 2021) by Sven Fuchs
RR by Anonymous Referee #1 (25 Aug 2021)
RR by Anonymous Referee #2 (26 Aug 2021)
ED: Publish subject to minor revisions (review by editor) (01 Sep 2021) by Sven Fuchs
AR by Lucas Wouters on behalf of the Authors (11 Sep 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (14 Sep 2021) by Sven Fuchs
AR by Lucas Wouters on behalf of the Authors (17 Sep 2021)  Manuscript 
Download
Short summary
This research introduces a novel approach to estimate flood damage in Malawi by applying a machine learning model to UAV imagery. We think that the development of such a model is an essential step to enable the swift allocation of resources for recovery by humanitarian decision-makers. By comparing this method (EUR 10 140) to a conventional land-use-based approach (EUR 15 782) for a specific flood event, recommendations are made for future assessments.
Altmetrics
Final-revised paper
Preprint